The Benefits of a Frailty Index for People With Intellectual Disability: A Commentary
Bibliographic record
Abstract
Abstract A frailty index is a quantitative measure of frailty, based on the nonspecific accumulation of deficits. In the general population the frailty index strongly predicts death and deterioration of independence and health. Few studies focus on frailty in people with intellectual disabilities (ID). However, because of the increasing longevity of people with ID, frailty will become a major healthcare challenge in this population. Here we argue the benefits of using a frailty index to measure frailty in people with ID, as it was suggested that this tool would lead to flawed results in this specific population. Most important, as the exact content of the frailty index is relatively free, the frailty index can include problems that are often present in the ID population, as well as use diagnostic questionnaires that have been validated in people with ID. In addition, as the frailty index is designed using a well‐evaluated standardized procedure, results can be compared to the general population. The frailty index is applicable to the ID population and predicts negative health outcomes, making it a useful instrument for policy and research. However, further research on its clinical applicability is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.042 | 0.049 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".